Tom Ryen

A rate-distortion optimal coding alternative to matching pursuit

This paper presents a method to find the operational rate-distortion optimal solution for an overcomplete signal decomposition. The idea of using overcomplete dictionaries, or frames, is to get a sparse representation of the signal. Traditionally, suboptimal algorithms, such as Matching Pursuit (MP), is used for this purpose. When using frames in a lossy compression scheme, the major issue is to find the best possible rate-distortion (RD) tradeoff. Given the frame and the Variable Length Code (VLC) table embedded in the entropy coder, the solution of the problem of establishing the best RD tradeoff has a very high complexity. The proposed approach reduces this complexity significantly by structuring the solution approach such that the dependent quantizer allocation problem reduces into an independent one. It is important to note that this large reduction in complexity is achieved without sacrificing optimality. The optimal rate-distortion solution depends on the VLC table embedded in the entropy coder. Thus, VLC optimization is part of this work. We show experimentally that the new approach outperforms Rate-Distortion Optimized Matching Pursuit, previously proposed in [1].

Eukaryotic Gene Prediction by Spectral Analysis and Pattern Recognition Techniques

The problem of computational gene prediction in eukaryotic DNA is investigated. The discrete Fourier transform is used to reveal the periodicity of three which is present in the essential subregions of a gene. We introduce a novel method that allows to predict the position of genes in an optimal way (in the sense of minimal error probability) based on the complex Fourier values at the frequency 1/3. Our method is based on training and testing a bayesian classifier. We simulate gene sequences for training, apply the Fourier transform to the sequences, extract feature vectors from the spectral representation of the binary sequences and train classifiers to discriminate coding from non coding regions in the sequence. The classifier is tested on a real gene sequence where the coding and non coding regions are known

Chest compression rate measurement from smartphone video

BackgroundOut-of-hospital cardiac arrest is a life threatening situation where the first person performing cardiopulmonary resuscitation (CPR) most often is a bystander without medical training. Some existing smartphone apps can call the emergency number and provide for example global positioning system (GPS) location like Hjelp 113-GPS App by the Norwegian air ambulance. We propose to extend functionality of such apps by using the built in camera in a smartphone to capture video of the CPR performed, primarily to estimate the duration and rate of the chest compression executed, if any.MethodsAll calculations are done in real time, and both the caller and the dispatcher will receive the compression rate feedback when detected. The proposed algorithm is based on finding a dynamic region of interest in the video frames, and thereafter evaluating the power spectral density by computing the fast fourier transform over sliding windows. The power of the dominating frequencies is compared to the power of the frequency area of interest. The system is tested on different persons, male and female, in different scenarios addressing target compression rates, background disturbances, compression with mouth-to-mouth ventilation, various background illuminations and phone placements. All tests were done on a recording Laerdal manikin, providing true compression rates for comparison.ResultsOverall, the algorithm is seen to be promising, and it manages a number of disturbances and light situations. For target rates at 110 cpm, as recommended during CPR, the mean error in compression rate (Standard dev. over tests in parentheses) is 3.6 (0.8) for short hair bystanders, and 8.7 (6.0) including medium and long haired bystanders.ConclusionsThe presented method shows that it is feasible to detect the compression rate of chest compressions performed by a bystander by placing the smartphone close to the patient, and using the built-in camera combined with a video processing algorithm performed real-time on the device.

A rate-distortion optimal alternative to matching pursuit

This paper presents a method to find the operational rate-distortion optimal solution for an overcomplete signal decomposition. The idea of using overcomplete dictionaries, or frames, is to get a sparse representation of the signal. Traditionally, suboptimal algorithms, such as matching pursuit (MP), are used for this purpose. When using frames in a lossy compression scheme, the major issue is to find the best possible rate-distortion (RD) tradeoff. Given the frame and the variable length code (VLC) table embedded in the entropy coder, the solution to the problem of establishing the best RD tradeoff is highly complex. The proposed approach reduces this complexity significantly by structuring the solution approach such that the dependent quantizer allocation problem reduces to an independent one. In addition, the use of a solution tree further reduces the complexity. It is important to note that this large reduction in complexity is achieved without sacrificing optimality. The optimal rate-distortion solution depends on the selection of the frame and the VLC table embedded in the entropy coder. Thus, frame design and VLC optimization is part of this work. We experimentally demonstrate that the new approach outperforms rate-distortion optimized (RDO) matching pursuit, previously proposed by Gharavi-Alkhansari.

Efficient frame vector selection based on ordered sets

The problem of finding the optimal set of quantized coefficients for a frame-based encoded signal is known to be of very high complexity. This paper presents an efficient method of finding the operational rate-distortion (RD) optimal set of coefficients. The major complexity reduction lies in the reformulation of the original RD-tradeoff problem, where a new set of coefficients is used as decision variables. These coefficients are connected to the orthogonalization of the set of selected frame vectors and not to the frame vectors themselves. By organizing all possible solutions as nodes in a solution tree, we use complexity saving techniques to find the optimal solution in an even more efficient way. Using an ordered vector selection process, the complexity can be again significantly reduced and efficient run-length encoding becomes feasible. Contrary to the original problem, the new problem can be solved optimally in a reasonable amount of time.